- release note: parallel takeaway + unmeasured-stats subsections for all three ladders (pixel takeaway now 0.961 >10 px / 0.986 >20 px, area stats now 83.5% ≤10 mm² / 89.0% ≤20 mm²); overall-recall column added and every value cited in prose bolded in its table; anisotropy section rewritten concisely with explicit sagittal/coronal naming; "Area is set by the floor" subsection and its median-floor table removed; release date stamped
- tl-stats README: the median-floor section now stands alone as the analysis record instead of describing the removed release-note table
[script,doc] feat: add the above-threshold recall figure, fold TL methodology into the tl-stats README
- tl_plots.py: new overview_recall_above_threshold figure — the same area/length panels with recall pooled over every cluster above each bin edge (the ">t" reading); empty bins keep their edge so thresholds never shift, and a zero-total tail goes NaN instead of a fake zero
- scripts/tl-stats/README.md: document the Feret length stratification (definition, provenance, both caveats), the release note's median-floor analysis, and the landmark-entry fields behind per-cluster attribution
- doc/release-v1.4.0.md: add >10 mm columns to the per-dataset recall table (corpus 0.967, none below 0.872); replace in-line methodology with pointers to the README; spell out the MedVision_DISABLE_SAMPLE_FILTERING opt-out
- doc/tl-annotation-stats-v1.4.0.md: all 12 datasets carry a detection plan; state how each plan vintage cross-checks the denominator
- regenerate the overview_major_axis and overview_recall figures alongside the new one
[script,doc] feat: stratify TL recall by physical area, add cumulative twins for all three ladders
- tl_stats.py: bin every cluster by area in mm2 (pixel count x the in-plane
voxel area of its own case, resolved per plane) alongside the pixel ladder;
bin_sizes generalises to bin_clusters over any column
- warn when a case carries no mask_file_info.voxel_size instead of letting it
drop out of the area grouping and shrink the denominator silently
- new cumulative_recall writes a corpus-level twin of each ladder reading the
same counts three ways: per bin, pooled <= threshold, pooled > threshold;
emitted for size, area, and length when tl_recall_by_length.csv is present
- tl_plots.py: left recall panel moves from pixels to mm2, and the overview box
plot widens to mirror full label names on a secondary right-hand axis
- doc: release note and stats report carry the three-way tables, gain the area
section relating recall to the resolution-adjusted floor rather than to area,
and are rewritten in a plain report register
- regenerate overview_recall and overview_major_axis (.…
[script,doc] feat: swap the TL recall figure to size and length panels, ship figures vector-only
- Replace the per-label/plane recall scatter in `overview_recall` with recall against
physical cluster length in mm from `tl_recall_by_length.csv`; recall vs cluster size
in pixels keeps the left panel
- Bin the length panel on the max Feret diameter of a cluster's pixel centres, not the
fitted major axis: an unmeasured cluster has no fitted axis, so only a mask-side
quantity can carry the denominator of a recall
- Pin `LENGTH_BIN_ORDER` locally — nothing in this repo writes `tl_recall_by_length.csv`,
so there is no producer to import the order from, and first-appearance ordering folds
the curve back on itself when a dataset is missing a bin
- Share one legend across the pair; both panels key colour on the same 12 datasets
- Drop `.png` output: `save_fig` writes `.svg` and `.pdf` for every figure from one place
so the formats cannot drift apart, and the stale PNGs are removed
- Record in the README that `tl_stats.py` does not write `tl_recall_by_length.csv`, so a
fresh …
[release] v1.4.0: floor tumour/lesion measurements on physical major axis, not pixel count
[Code]
- add --cluster_filter_method {major_axis,pixel_count} to the fromSeg biometry planner and
to all 12 T/L preprocess_biometry.py; default major_axis
- major_axis measures a cluster when its FITTED major axis clears
max(MIN_MAJOR_AXIS_MM, 2 x coarser in-plane spacing). A pixel count is not a physical
size: sagittal and coronal are reconstructed across the slice axis, so on a
0.977/0.977/3.0 mm case a 20 px floor discards below ~4.9 mm axially but ~8.6 mm
sagittally.
- MIN_MAJOR_AXIS_MM = 2.0 uniformly across every dataset and target label: it admits nearly
every annotated lesion and binds only where in-plane spacing is under 1 mm. Clinical
floors (RECIST 10 mm, Fleischner 6 mm, BI-RADS 5 mm) are per-LESION thresholds on one
largest cross-section whereas this filter is per-slice, so they are left to downstream
consumers rather than encoded here
- guard the fit itself, now that major_axis removes the pre-fit size test. cv2.fitEllipse
is a least-squares conic in f…
